Many companies have a significant interest in obtaining information that indicates the relative financial risk or profitability of potential business transactions with individuals or other entities. For example, a lending institution may be interested in the relative likelihood that a loan recipient will timely and reliably make the agreed-upon loan payments. An insurance company may be interested in the relative likelihood that an existing or potential client may file certain claims. Such predictive information can be used to decide whether a company should engage in a particular business transaction and/or the terms that should be used for the transaction.
A large variety of public records and privately developed databases can be utilized to inform such risk/benefit determinations. For example, credit reporting agencies (CRAs) collect and maintain information about a person's individual credit history and the person's accounts. This information can include, for example, total credit line for each account, current credit balance for each account, credit ratios, whether an account is in good standing, whether there have been delinquent payments on an account, the date when an account was opened, records of recent and/or historical inquiries into the person's credit, and so forth. Such information is also available for groups of individuals as well as entities. However, the extensive amount of data available for any given person or entity makes the task of evaluating a business decision based purely on raw credit data very difficult.